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Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors. [ABSTRACT FROM AUTHOR]

With the escalating challenges of climate change and the imperative for carbon neutrality, evaluating the carbon footprint of regional power systems becomes crucial. To address this issue, a low-carbon evaluation framework for regional power systems is developed. First, this study identifies the fundamental requirements that the index system for the low-carbon power system should follow and specifies the general direction for the index system's creation. Second, an evaluation model for carbon reduction ability is proposed by considering generation side, grid side and load side of the power system. Then, a comparative evaluation model for the carbon reduction capability of multi-regional power systems is proposed to spatiotemporally compare the carbon reduction capability of different power systems. Next, a quantification method for the weights of low-carbon evaluation indicators is established based on the power system simulation of multi-scenario scheduling and index sensitivity analysis method. Finally, a low-carbon comprehensive score for regional power systems is created by combining the evaluation results of the system's carbon reduction capacity with index weights. The practical research results in a region of Zhejiang province in China demonstrate that the proposed model can provide a reasonable and feasible evaluation plan for the low-carbon construction of regional power systems. [ABSTRACT FROM AUTHOR]

Dimethyl sulfide (DMS), an organic volatile sulfide produced from Dimethylsulfoniopropionate (DMSP), exerts a significant impact on the global climate change. Utilizing published literature data spanning from 2005 to 2020, a BP neural network (BPNN) model of the surface seawater DMS in the Yellow and East China Sea (YECS) was developed to elucidate the influence of various marine factors on the DMS cycle. Results indicated that the six parameters inputted BPNN model, that include the time (month), latitude and longitude, sea-surface chlorophyll a (Chl-a), sea-surface temperature (SST), and sea-surface salinity (SSS), yielded the optimized simulation results (R2 = 0.71). The optimized estimation of surface seawater DMS in the YECS were proved to be closely aligned with the observed data across all seasons, which demonstrated the model’s robust applicability. DMS concentration in surface seawater were found to be affected by multiple factors such as Chl-a and SST. Comparative analysis of the three environmental parameters revealed that Chl-a exhibited the most significant correlation with surface seawater DMS concentration in the YECS (R2 = 0.20). This underscores the pivotal role of chlorophyll in phytoplankton photosynthesis and DMS production, emphasizing its importance as a nonnegligible factor in the study of DMS and its sulfur derivatives. Furthermore, surface seawater DMS concentration in the YECS exhibited positive correlations with Chl-a and SST, while displaying a negative correlation with SSS. The DMS concentration in the YECS show substantial seasonal variations, with the maximum value (5.69 nmol/L) in summer followed in decreasing order by spring (3.96 nmol/L), autumn (3.18 nmol/L), and winter (1.60 nmol/L). In the YECS, there was a gradual decrease of DMS concentration from the nearshore to the offshore, especially with the highest DMS concentration concentrated in the Yangtze River Estuary Basin and the south-central coastal part off the Zhejiang Province. Apart from being largely composed by the release of large amounts of nutrients from anthropogenic activities and changes in ocean temperature, the spatial and temporal variability of DMS may be driven by additional physicochemical parameters. [ABSTRACT FROM AUTHOR]

Global warming is caused by rising carbon dioxide concentrations, while ecological and environmental issues are becoming increasingly evident. Many governments' main objective in addressing climate challenges is to achieve carbon balance. Exploring the distribution and changing trends of carbon balance can contribute to carbon balance strategies. This study takes the Yangtze River Economic Belt (YREB) as the study area, analyzing the spatial and temporal dynamic changes in carbon emissions and carbon sequestration in the YREB from 2000 to 2017. The county-level supply–demand ratios and accessibility are calculated based on the enhanced two-step floating catchment method according to the dynamic flow radius. According to our experimental results, carbon emissions can achieve different degrees of carbon balance according to the flow radius. After carbon emissions flow between counties within the province, some provinces can realize the transition from carbon surplus to carbon balance. With the expansion of the flow radius and the increase in internal mobility, more counties can participate in carbon sequestration, and some provinces can achieve carbon neutrality after carbon emissions transfer across provinces. Hubei, Chongqing, Zhejiang, Hunan and other provinces with special carbon sequestration capacity are very sensitive to changes in the radius of carbon emission flow. In 2017, when the carbon emission flow radius is 50 km, the net carbon emission of the flow can be reduced by 3%. If the radius is increased to 200 km, the YREB can achieve carbon balance. This study can provide decision-making information for carbon emission ecosystem management in the YREB. [ABSTRACT FROM AUTHOR]

Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Predicting, mapping, and evaluating the flood inundation risk (FIR) is important for coastal tourism. This study developed a spatial tourism-aimed framework by integrating a Weighted k Nearest Neighbors (WkNN) algorithm, geographic information systems, and environmental indexes, such as precipitation and soil. These model inputs were standardized and weighted using inverse distance calculation and integrated into WkNN to infer the regional probability and distribution of the FIR. Zhejiang province, China, was selected as a case study. The evaluation results were mapped to denote the likelihood of an FIR, which was then validated by the historical Maximum Inundation Extent (MIE) extracted from the World Environment Situation Room. The results indicated that 80.59% of the WkNN results reasonably confirmed the MIE. Among the matched areas, 80.14%, 90.13%, 65.50%, and 84.14% of the predicted categories using WkNN perfectly coincided with MIE at high, medium, low, and very low risks, respectively. For the entire study area, approximately 2.85%, 64.83%, 10.8%, and 21.51% are covered by a high, medium, low, and very low risk of flood inundation. Precipitation and elevation negatively contribute to a high-medium risk. Drainage systems positively alleviate the regional stress of the FIR. The results of the evaluation illustrate that in most inland areas, some tourism facilities are located in high-medium areas of the FIR. However, most tourism facilities in coastal cities are at low or very low risk, especially from Hangzhou-centered northern coastal areas to southern Wenzhou areas. The results can help policymakers make appropriate strategies to protect coastal tourism from flood inundation. Moreover, the evaluation accuracy of WkNN is higher than that of kNN in FIR. The WkNN-based framework provides a reasonable method to yield reliable results for assessing FIR. The framework can also be extended to other risk-related research under climate change. [ABSTRACT FROM AUTHOR]

The 4.2 ka event was a period of global drought and climate cooling linked to the widespread disruption of Neolithic culture. However, the detailed climatic changes during this period are not completely understood. In this paper, we report a high-resolution pollen record for the Dayanghu wetland in the Zhejiang region of southeastern China, which we use to infer mean annual precipitation (MAP) fluctuations during the period spanning 4600–4000 calendar years before present (cal. yr BP). There was a long-term trend of decreasing MAP during this interval, consistent with weakening of the East Asian Summer Monsoon since the mid-Holocene. This trend was punctuated by episodes of relatively high MAP at 4350–4320, ca. 4260, and 4220–4200 cal. yr BP, and extreme droughts at 4200 and 4150 cal. yr BP. We propose that solar activity promoted El Niño events and contributed to dramatic MAP fluctuations in the Dayanghu wetland. La Niña events and negative-phase intervals of the Pacific Decadal Oscillation may have been responsible for the extreme drought at 4200–4150 cal. yr BP. In summary, we propose a close relationship between MAP and solar forcing, with short-term enhanced solar activity increasing the intensity of the East Asian Summer Monsoon and contributing to increasing decadal variability in the inner tropical Pacific, which ultimately leads to increased precipitation variability in southeast China. The results improve our understanding of the detailed climatic features of the 4.2 ka event. • High-resolution pollen record for ca. 4.2 ka for the Zhejiang region, China. • Pollen-based mean annual precipitation was quantitatively reconstructed. • Long-term droughts tended to occur during 4600–4000 cal. yr BP. • La Niña-dominated ENSO contributed to a catastrophic drought at 4200–4150 cal. yr BP. [ABSTRACT FROM AUTHOR]

Accurate, seamless, and long-term land surface temperature (LST) data sets are crucial for investigating climate change and agriculture production. However, factors like cloud contamination have led to invalid values in the LST product, which has restricted the application of the LST dataset. Therefore, the reconstruction of LST products is challenging, and it is attracting widespread attention. This study compared the performance of different algorithms (XGBoost, GBDT, RF, POLY, MLR) and different training sets (using only good-quality pixels or using both good-quality and other-quality pixels) in the estimation of missing pixels in the LST data, obtaining a seamless daily 1 km LST dataset of MODIS Terra-day, Aqua-day, Terra-night, and Aqua-night data for Zhejiang Province and its surrounding areas from 2000 to 2022. The results demonstrated that the performance of machine-learning models is significantly better than that of linear models, and among the five models, XGBoost performed the best, with an RMSE of less than 1 °C. The Wilcoxon test between the reconstructed LST and the true LST values revealed that including both good-quality and other-quality pixels for reconstruction resulted in a 33% increase in the number of days with non-significant differences compared with using only good-quality pixels. Moreover, the reconstructed nighttime LST has a lower RMSE compared with the reconstructed daytime LST, and the RMSE of the reconstructed LST on the Terra satellite is lower than the RMSE of the reconstructed LST on the Aqua satellite. The RMSEs for the reconstructed LSTs are 0.50 °C, 0.61 °C, 0.36 °C, and 0.39 °C, corresponding to Terra-day, Aqua-day, Terra-night, and Aqua-night for images with coverage reaching 70%, 0.65 °C, 0.83 °C, 0.49 °C, respectively, and 0.52 °C for images with coverage less than 70%. The accuracy of the reconstructed LSTs using our proposed framework outperforms the existing reconstruction methods. The 1 km daily seamless LST products can be applied in various fields, such as air temperature estimation, climate change, urban heat island, and crop temperature stress monitoring. [ABSTRACT FROM AUTHOR]

As global warming intensifies, plant carbon sequestration is becoming increasingly important. Studies have shown that Ginkgo biloba L. is a kind of tree with a high carbon sequestration capacity and is one of the dominant tree species suitable for urban ecosystems. Predicting the changes in the potential suitable areas, carbon sequestration amount and carbon sink value of G. biloba under current and future climate change will provide some references for the resource utilization of urban green tree species and the realization of carbon sink value. In this study, we used 'kuenm', an R package that uses MaxEnt, as a modeling algorithm to predict the potential suitable areas of G. biloba under nine climate scenarios, and the carbon sequestration and the carbon sink values under the fair-value measurement model were calculated. We found that: (1) the optimized MaxEnt model improved the prediction accuracy (AUC = 0.97 ± 0.004) significantly. (2) The total current potential suitable area of G. biloba was 175.11 × 104-km2, representing 21.63% of China's total territorial area, and the highly suitable area was 26.86 × 104-km2, accounting for 2.83% of China's total territorial area, concentrated primarily in the regions of Sichuan and Chongqing, southern Jiangsu, and Zhejiang Province. (3) The eight future climate scenarios predict that the suitable area of G. biloba will initially decrease and then increase, and the newly expanded area will be distributed primarily in the middle and upper reaches of the Yangtze River as well as near the estuary of the Yangtze River, while the region suffering losses will move from Sichuan and Chongqing to Hunan, Jiangxi and Zhejiang Province as well. Hf and bio2 are the major factors affecting the distribution of G. biloba. (4) The total annual carbon sequestration of G. biloba was 1.60 × 106-t, 1.50 × 106-t and 1.57 × 106-t under different environmental conditions in the present, the 2050s and the 2090s, respectively. Over these three time periods, we obtained total carbon sequestration values of 5.63 × 107 CNY, 5.28 × 107 CNY and 5.53 × 107 CNY. [ABSTRACT FROM AUTHOR]

With global climate change and rapid urbanization, it is critical to assess urban flood resilience (UFR) within the social-economic-natural complex ecosystem in dealing with urban flood disasters. This research proposes a conceptual framework based on the PSR-SENCE model for evaluating and exploring trends in urban flood resilience over time, using 27 cities in the Yangtze River Delta (YRD) of China as case studies. For the overall evaluation, a hybrid weighting method, VIKOR, and sensitivity analysis were used. During that time, UFR in the YRD region averaged a moderate level with an upward trend. This distinguishes between the resilience levels and fluctuation trends of provinces and cities. Jiangsu, Zhejiang, and Anhui provinces all displayed a trend of progressive development; however, Shanghai displayed a completely opposite pattern, mainly because of resilience in the state dimension. During that time, 81.41% of cities exhibited varying, upward trends in urban flood resistance, with few demonstrating inverse changes. Regional, provincial, and city-level implications are proposed for future UFR enhancement. The research contributes to a better understanding of the urban complex ecosystem under flood conditions and provides significant insights for policymakers, urban planners, and practitioners in the YRD region and other similar flood-prone urban areas. [ABSTRACT FROM AUTHOR]

Subtropical forests play an important role in the global carbon cycle and climate change mitigation. In order to understand the effects of climate factors on soil carbon in subtropical forest ecosystems, it is necessary to make full use of carbon sequestration potential. Soil organic carbon (SOC) and soil alkali-hydrolyzed nitrogen (SAN) were tested in 255 plots of subtropical forests in Zhejiang Province, and their forest reserves from 2020 in Zhejiang Province were compared with those from 2010. The results showed that SOC content significantly increased, but SAN content decreased over those ten years. Combined with random forest (RF) and correlation analysis, the contribution of different climate factors (temperature, precipitation, etc.) to soil carbon storage was analyzed, and the main driving factors were evaluated. The RF model explained that winter (December to February) and spring (March to May) were the most dominant drivers to the 0–10 cm and 10–30 cm increases in SOC. There was a significant positive correlation between precipitation and SOC accumulation (0–30 cm) during winter and spring. The minimum temperatures in summer (June to August) and autumn (September to November) were negatively correlated with SOC accumulation (0–30 cm). Increasing the precipitation or irrigation (cloud seeding) in winter could improve the carbon sequestration capacity of subtropical forest soils. This study provides a new perspective on the sensitivity and potential response of the carbon cycle to climate change in subtropical forest ecosystems. [ABSTRACT FROM AUTHOR]

With the continuous development of industry, the massive emission of carbon dioxide (CO2) has become a global challenge that cannot be ignored due to its nature as the primary driver of global climate change and environmental crisis. Carbon neutrality is proposed as a global initiative to mitigate climate change. As a developing industrial country, China provides about one-third of global carbon emissions and has set a carbon-neutral goal by 2060. To achieve this goal, continuous efforts across the country are required. In this review, Zhejiang Province, one of the most developed regions in China, is set as a model for analysis. We first summarize the current situation of carbon emission in Zhejiang Province. Then, corresponding sustainable approaches such as ecological and industrial solutions for the reduction of future carbon emissions are introduced for Zhejiang Province. We also provide a direction for the realization of carbon neutrality, focusing on the most promising solutions for Zhejiang Province. [ABSTRACT FROM AUTHOR]

Climate change and rapid urban expansion can foster landslide disasters and cause severe damage in areas that are traditionally considered relatively safe from such hazards. In this regard, a recent event in Daoshi Town, China, is a good example that showcases the combined effect of extreme rainfall and urbanization in generating a first-time landslide disaster for a developing community. On 10 August 2019, Typhoon Lekima made its landing in Zhejiang Province, China. It generated a rainstorm close to Daoshi Town, inducing severe and unexpected damage from shallow landslides and debris flows (7 deaths, 25,560 affected and a direct economic loss of 1.5 hundred million dollars). In this region, similar rainfall has never been experienced before, and landslides have seldom been reported in the past. Field investigations and measurements were conducted to identify the landslide distributions, size characteristics, and mobility. A landslide inventory map, including 251 shallow landslides and 82 debris flows, was compiled to study the geological and topographical factors related to widespread failure. The results showed that shallow landslides and debris flows mainly occurred in the north-central part of the study area, in which the base was mainly composed of strongly weathered limestone. The elevation, mean slope, and slope aspect also contributed to the occurrence of landslides. In addition, 32.4% of the disasters were located close to buildings and roads, highlighting a relevant predisposing role played by human activity. This typhoon-bound rainstorm gave rise to a higher triggering condition than the normally required rainfall threshold in the study area. Under this circumstance, landslides were easily triggered and were able to transition into debris flows due to liquefaction. Field observations demonstrate that the erosion of slope toes caused by debris flows also facilitated the occurrence of other shallow landslides. A changing climatic setting is a never-before-experienced phenomenon for the study area. This research improves the understanding of landslide development in the area and prepares basic data and information for further studies and mitigation strategies. [ABSTRACT FROM AUTHOR]

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province. [ABSTRACT FROM AUTHOR]

Energy consumption is a major factor influencing climate change. Low-carbon strategies can be defined from carbon (C) footprint and ecological pressure caused by energy consumption. We hypothesized that this should be done at the regional and city levels because factors controlling C footprints vary with location and time, yet there is actually little knowledge at local levels. Here, we mapped the C emissions and footprint of 11 cities in the Zhejiang Province of China, a rapidly growing area of about 57 million inhabitants, which is representative of the strong economic growth in China. The C footprint is expressed as the land surface necessary to adsorb C emissions. We studied spatio-temporal evolution of C footprints from 2005 to 2015 using the center of gravity method. Results show that total annual C emissions due to energy consumption in Zhejiang increased by 40.83 million tons (55.8%) from 2005 to 2011. Then, from 2011 to 2015, C emissions decreased by 5.8%, which is explained by the 5-year national plan aiming at decreasing emissions. Similarly, the total C footprint increased by 55.8% from 29,201,904 hm2 in 2005 to 45,496,817 hm2 in 2011 and then declined by 5.8% from 2011 to 2015. At the city level, Ningbo has the largest C footprint contribution, whereas Lishui the lowest. Zhoushan shows an increase in C emissions and footprint, whereas Hangzhou and Shaoxing show a decline. The center of gravity of the C footprint is located in Shaoxing and is moving southeast from 2006 to 2011. Overall, our findings reveal the high spatial heterogeneity of C footprint at the regional and city levels, implying that national low-carbon strategies should be adapted at lower levels for higher efficiency. [ABSTRACT FROM AUTHOR]

The climate change characteristics and adaptation abilities of the offshore East China Sea area are very important issues against the backdrop of the Maritime and Polar Silk Roads strategy. Based on meteorological observation data and reanalysis data, the spatial distribution and temporal variation characteristics of sea surface temperature (SST), sea surface salinity (SSS) and winds in the offshore East China Sea area were analyzed. From 1979 to 2017, the offshore average SST increased by 0.18°C/10 a as a whole. The average SSS showed an interdecadal cycle from 1980 to 2017 with a peak in 2005 (34.54 g/kg) and a trough in 1995 (34.26 g/kg). The lowest offshore SSS was 34.0 g/kg in the southern Korean Peninsula and the southern Taiwan Strait, while the highest offshore SSS was 34.8 g/kg in the low-latitude Pacific offshore area. The climatic characteristics annual accumulated gale days, annual local maximum wind speed, absolute maximum wind speed and average wind speed from 1971 to 2017 were analyzed, which showed that the influence of gales on Zhejiang was more serious than their influence on the other provinces in East China. In addition, decision-making strategies for climate change adaptation are discussed to provide a scientific reference for controlling the risks of climate change to the East China coastal zone and the Maritime Silk Road strategy. [ABSTRACT FROM AUTHOR]

Ecosystem-scale water-use efficiency (WUE) is an important indicator for understanding the intimately coupled relationship between carbon and water cycles in ecosystems. Previous studies have suggested that both abiotic and biotic factors have significant effects on WUE in forest ecosystems. However, responses of WUE to phenology in the context of climate change remain poorly understood. In this study, we analyzed the sensitivity and response patterns of seasonal WUE to phenology in Zhejiang Province where typical subtropical forest ecosystems are located, and discussed potential causes of the changes of the sensitivity and response patterns along different climate gradient during 2000–2014. The results of interannual partial correlation analysis showed widespread negative correlations between WUE and the start of growing season (SOS) in spring. This is because the increase in gross primary product (GPP) is larger than that of evapotranspiration (ET), resulting from an advanced SOS. The positive correlation between WUE and SOS was widely observed in summer mainly because of water stress and plant ecological strategy. The autumn WUE enhanced with the delay in the end of growing season (EOS) mainly because of the increase in GPP meanwhile the decrease or steadiness in ET, resulting from a delayed EOS. In space, the sensitivity of spring WUE to SOS significantly decreased along the radiation gradient, which might be related to strong soil evaporation in high radiation area; the sensitivity of WUE to SOS in summer showed a positive correlation with precipitation and a negative correlation with temperature, respectively, which might be attributed to the compensation of GPP to the delayed SOS and water stress caused by high temperature. The sensitivity of WUE to EOS increased significantly along the radiation and precipitation gradients in autumn, which may be because the increase of radiation and precipitation provides more water and energy for photosynthesis. [ABSTRACT FROM AUTHOR]

Subtropical forests have great potential as carbon sinks; however, the relationship between net ecosystem productivity (NEP) and climate change is still unclear. This study took Zhejiang Province, a subtropical region, as an example. Based on remote sensing classification data of forest resources, the integrated terrestrial ecosystem carbon cycle (InTEC) model was used to simulate the spatiotemporal dynamics of the forest NEP in Zhejiang Province during 1985–2015 and analyze its response to meteorological factors such as temperature, precipitation, relative humidity, and radiation. Three patterns emerged: (1) The optimized InTEC model can better simulate the forest NEP in Zhejiang Province, and the correlation coefficient between the simulated NEP and observed NEP was up to 0.75. (2) From 1985 to 2015, the increase in the total NEP was rapid, with an average annual growth rate of 1.52 Tg·C·yr−1. During 1985–1988, the forests in Zhejiang Province were carbon sources. After 1988, the forests turned into carbon sinks and this continued to increase. During 2000–2015, more than 97% of the forests in Zhejiang Province were carbon sinks. The total NEP reached 32.02 Tg·C·yr−1, and the annual mean NEP increased to 441.91 gC·m−2·yr−1. The carbon sequestration capacity of forests in the east and southwest of Zhejiang Province is higher than that in the northeast of Zhejiang Province. (3) From 2000 to 2015, there was an extremely significant correlation between forest NEP and precipitation, with a correlation coefficient of 0.85. Simultaneously, the forest NEP showed a negative correlation with temperature and radiation, with a correlation coefficient of −0.56 for both, and the forest NEP was slightly negatively correlated with relative humidity. The relative contribution rates of temperature, precipitation, relative humidity, and radiation data to NEP showed that the contribution of precipitation to NEP is the largest, reaching 61%, followed by temperature and radiation at 18% and 17%, respectively. The relative contribution rate of relative humidity is the smallest at only 4%. During the period of 1985–1999, due to significant man-made disturbances, the NEP had a weak correlation with temperature, precipitation, relative humidity, and radiation. The results of this study are important for addressing climate change and illustrating the response mechanism between subtropical forest NEP and climate change. [ABSTRACT FROM AUTHOR]